방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| Elastic Net× | 로지스틱 회귀× | |
|---|---|---|
| 분야≠ | 머신러닝 | 연구 통계 |
| 계열≠ | Machine learning | Process / pipeline |
| 기원 연도≠ | 2005 | 1958 |
| 창시자≠ | Zou, H. & Hastie, T. | David Roxbee Cox |
| 유형≠ | Regularized linear regression (L1 + L2 penalty) | Method |
| 원전≠ | Zou, H. & Hastie, T. (2005). Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society: Series B, 67(2), 301–320. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| 별칭≠ | Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression | logit model, binomial logistic regression, LR |
| 관련≠ | 4 | 3 |
| 요약≠ | Elastic Net is a regularized linear regression method introduced by Zou and Hastie in 2005 that blends the LASSO (L1) and Ridge (L2) penalties, so it performs variable selection and coefficient shrinkage at the same time. It is designed for predictive and explanatory modelling on data with many, possibly correlated, predictors. | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. |
| ScholarGate데이터셋 ↗ |
|
|